RNA Secondary Structure Prediction with Simple Pseudoknots Based on Dynamic Programming

Abstract

RNA molecules are sequences of nucleotides that serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic molecules. The sequence of nucleotides of an RNA molecule constitutes its primary structure, and the pattern of pairing between nucleotides determines the secondary structure of an RNA. Computational prediction of RNA secondary structure is among the few structure prediction problems that can be solved satisfactory in polynomial time. Most work has been done to predict structures that do not contain pseudoknots. Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modelling. In this paper, we present a computation the maximum number of base pairs of an RNA sequence with simple pseudoknots. Our approach is based on pseudoknot technique proposed by Akutsu. We show that a structure with the maximum possible number of base pairs could be deduced by a improved Nussinov’s trace-back procedure. In our approach we also considered wobble base pairings (G·U). We introduce an implementation of RNA secondary structure prediction with simple pseudoknots based on dynamic programming algorithm. To evaluate our method we use the 15 sequences with simple pseudoknots of variable size from 19 to 25 nucleotides. We get our experimental data set from PseudoBase. Our program predicts simple pseudoknots with correct or almost correct structure for 53% sequences.